Slava Razbash, has worked in data science roles in multinational enterprises,startups and even a university. He has a solid track record that includes working in CBA’s big data team and helping start Sportsbet’s datascience and personalisation capability. Slava is the Founder of the Enterprise Data Science Architecture Conference.

Reserve your place today at https://edsaconf.io because you must keep your skills current.

The distinction between “supervised” and “unsupervised” learning has existed for a long time. Only recently however, has there been such a storm of misunderstanding about what these terms actually mean. Read this article to help you discern which consultants your organisation needs to fire.

Learn to discern the wolves

A supervised learningproblem has labels and features. Labels are what you want to predict. Features are the inputs that you will have access to at the time when you make a prediction. What you are asking is “learn to predict these outcomes.” We have some examples below to make it concrete.

A real estate automated valuation model (AVM). The labels are the prices at which houses have previously sold for. The features are location, number of bedrooms, number of bathrooms, land size, …etc.

A credit risk model. The labels are whether or not a customer is deemed to have defaulted on their loan. The features are time with bank, number of credit enquiries, income, living expenses…etc.

Sales forecasting model. The labels are past sales numbers. The features are also past sales numbers. This is a time series forecasting example.

Image recognition. The labels are the description of what is in the image. These are usually discrete categories. For example, “cat”, “hot dog”, “tiger” and “number 7”. The features are the images.

A unsupervised learning problem does not have labels. Only features. What you are asking is “find me some interesting patterns in this data.” We have to think harder to come up with some good examples – see below.

Customer feedback text clustering. Our company has a free text feedback form. We take customers’ text feedback and assign it to several groups. We then read a few responses from each group to understand the nature of the feedback in that group.

Suppose that we don’t have house prices, but we still want to see what kind of houses we can buy. The features are location, number of bedrooms, number of bathrooms, land size, …etc. So we investigate further and examine how big the land size is in different suburbs.

Customer segmentation. Assign customers to segments. The marketing team uses the segments to plan their marketing strategy.

Productionise Properly – come see how it’s done. The Enterprise Data Science Architecture Conference focuses on how to properly productionise data science solutions at scale. We have confirmed speakers from ANZ Bank, Coles Group, SEEK, ENGIE, Latitude Financial, Microsoft, AWS and Growing Data. The combination of presentations is intended to paint a complete picture of what it takes to productionise a profitable data science solution. As an industry, we are figuring out how to best build end-to-end machine learning solutions. As the field matures, knowledge of best practices in end-to-end machine learning pipelines will become essential skills. I invite you to view our list of confirmed speakers and talks at https://edsaconf.io because this is the right place to meet the right people and up-skill.

Meet the right people and up-skill. The conference will be on the 27th March at the Melbourne Marriott Hotel. A fully catered conference with coffee, lunch, morning/afternoon tea and evening drinks & canapes. I invite you to reserve your place at https://edsaconf.io this is the best place to learn the emerging best practices.

Slava Razbash, has worked in data science roles in multinational enterprises,startups and even a university. He has a solid track record that includes working in CBA’s big data team and helping start Sportsbet’s datascience and personalisation capability. Slava is the Founder of the Enterprise Data Science Architecture Conference.

Reserve your place today at https://edsaconf.io because you must keep your skills current.

You may be wondering “What is this data science thing and how does it help my business?” You may have built a career in an established branch of IT. You may be a business professional with a solid grounding in what makes your business work. Many non-specialist articles and presentations have made general statements such as “AI will revolutionise industry [X]”. In other words, “all fluff”.

This article cuts through the hype and provides a concrete example of how data science can be used to add business value. No fluff.

We will walk through a hypothetical scenario that is likely to be profitable if implemented. The terms “AI”, “machine learning” and “data science” are used as synonyms.

Here’s our scenario: We run a sports betting business. Customers can place bets on our website, mobile app or in our physical store. If we can recommend the right sports and bet types, at the right time, to the right customer, then we can increase the number of bets that a customer places. More bets leads to more turnover and lower variance, which lead to higher profits.

But why use machine learning? If each customer were to bring us a seven figure profit each year, then we can afford a relationship manager for each customer. No need for machine learning. However, each customer only brings in a few dollars of profit. We aim to spend a few cents per recommendation, per customer, to earn an additional dollar per customer. If we get this right, then we can spend a few cents to earn an extra doller per customer. Each customer would spend more money with us and our total profits would increase. So how do we do it?

A modification to the front to display the recommendations that they are instructed to display.

A machine learning model that decides which recommendations to serve and to whom.

Dashboards for reporting campaign performance and ROI.

An end-to-end pipeline for building machine learning models, deploying them and tracking their ROI versus control groups. These pipelines are sometimes referred to as MLOps.

How to integrate all of these components together? This is a massive topic that we will start exploring in the next article.

The Enterprise Data Science Architecture Conference will present real solutions that have been deployed in large companies. I invite you to reserve your place now because it is the best place to learn the emerging best practices.

Machine Learning in Production. The Enterprise Data Science Architecture Conference focuses on how to properly productionise data science solutions at scale. We have confirmed speakers from ANZ Bank, Coles Group, SEEK, ENGIE, Latitude Financial, Microsoft, AWS and Growing Data. The combination of presentations is intended to paint a complete picture of what it takes to productionise a profitable data science solution. As an industry, we are figuring out how to best build end-to-end machine learning solutions. As the field matures, knowledge of best practices in end-to-end machine learning pipelines will become essential skills. I invite you to view our list of confirmed speakers and talks at https://edsaconf.io because you must keep you skills current.

Meet the right people and up-skill. The conference will be on the 27th March at the Melbourne Marriott Hotel. A fully catered conference with coffee, lunch, morning/afternoon tea and evening drinks & canapes. I invite you to reserve your place at https://edsaconf.io this is the best place to learn the emerging best practices.